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airquality

assignment-21-a-b

assignment-q24

assignment-q9_b

assignment-q_20

assignment-set2-q_1

assignment-set2-q_2

The current age (in years) of 400 clerical employees at an insurance claims processing center is normally distributed with mean = 38 and Standard deviation =6. For each statement below, please specify True/False. If false, briefly explain why.

assignment-set2-q_3

assignment-set2-q_4

Let X ~ N(100, 202). Find two values, a and b, symmetric about the mean, such that the probability of the random variable taking a value between them is 0.99.

assignment-set2-q_5

Consider a company that has two different divisions. The annual profits from the two divisions are independent and have distributions Profit1 ~ N(5, 32) and Profit2 ~ N(7, 42) respectively. Both the profits are in $ Million. Answer the following questions about the total profit of the company in Rupees. Assume that $1 = Rs. 45 A. Specify a Rupee range (centered on the mean) such that it contains 95% probability for the annual profit of the company. Ans: Range is Rs (-77.38865513011706, 1157.388655130117) in Millions.

assignment-set3-q_5

In January 2005, a company that monitors Internet traffic (WebSideStory) reported that its sampling revealed that the Mozilla Firefox browser launched in 2004 had grabbed a 4.6% share of the market. I. If the sample were based on 2,000 users, could Microsoft conclude that Mozilla has a less than 5% share of the market? II. WebSideStory claims that its sample includes all the daily Internet users. If that’s the case, then can Microsoft conclude that Mozilla has a less than 5% share of the market?

assignment-set4-q_3

Auditors at a small community bank randomly sample 100 withdrawal transactions made during the week at an ATM machine located near the bank’s main branch. Over the past 2 years, the average withdrawal amount has been $50 with a standard deviation of $40. Since audit investigations are typically expensive, the auditors decide to not initiate further investigations if the mean transaction amount of the sample is between $45 and $55. What is the probability that in any given week, there will be an investigation? A. 1.25% B. 2.5% C. 10.55% D. 21.1% E. 50%

assignmentq9_a

association-rules-assignemnt

clustering-assignment

decision-trees-assignment

eda

fakenews-detection

What is Fake News? A type of yellow journalism, fake news encapsulates pieces of news that may be hoaxes and is generally spread through social media and other online media. This is often done to further or impose certain ideas and is often achieved with political agendas. Such news items may contain false and/or exaggerated claims, and may end up being viralized by algorithms, and users may end up in a filter bubble.

forecasting-assignment

hypothesis-testing

hypothesis-testing-assignment

Hypothesis Testing Assignment

knn-assignment

logistic-regression-assignment

multi-linear-regression

naive-bayes-assignment

neural-networks

normal-distribution

parkinsons-disease-detection

What is Parkinson’s Disease? Parkinson’s disease is a progressive disorder of the central nervous system affecting movement and inducing tremors and stiffness. It has 5 stages to it and affects more than 1 million individuals every year in India. This is chronic and has no cure yet. It is a neurodegenerative disorder affecting dopamine-producing neurons in the brain. What is XGBoost? XGBoost is a new Machine Learning algorithm designed with speed and performance in mind. XGBoost stands for eXtreme Gradient Boosting and is based on decision trees. In this project, we will import the XGBClassifier from the xgboost library; this is an implementation of the scikit-learn API for XGBoost classification. Detecting Parkinson’s Disease with XGBoost – Objective To build a model to accurately detect the presence of Parkinson’s disease in an individual. Detecting Parkinson’s Disease with XGBoost – About the Python Machine Learning Project In this Python machine learning project, using the Python libraries scikit-learn, numpy, pandas, and xgboost, we will build a model using an XGBClassifier. We’ll load the data, get the features and labels, scale the features, then split the dataset, build an XGBClassifier, and then calculate the accuracy of our model.

pca-assignment

plot