Supervised Learning - Prediction Week 3 Challenge

Uber Supply and Demand Gap Analysis  

COVID-19 Vaccinations Trend Analysis  

Project 2 - Create a report using PowerQuery, Macro VBA, List Functions and Data Validation functions for Data Reporting of Supply Chain Management companies           Data Summary of All Female Client’s for Income and Loan Amount using New Functions         …

Project 1 - Data Cleaning and Transformation using Pivot table and Charts for the Report on the requirement of Team Hiring  

Project 2  

Project 1 (Suppose you are appointed as a Data scientist in any Pharma Company. That company makes medicine for heart disease. Your senior manager has given several clinical parameters about a patient, can you predict whether or not the patient has heart disease?There are following thirteens clinical parameters of…

Project 2 - Gender Bias in Science and Technical field

  Project 1 - Analyzing the Education trends in Tamilnadu     Women in STEM Fields https://public.tableau.com/shared/WRHKS84P3?:display_count=n&:origin=viz_share_link

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dear team kindly find the attachment of project 1 with part a and partb  1A: English Dictionary App 1B: Library Book Management System

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QUESTION1 ANS: Explanation In Statistics the term sampling refers to selection of a part of aggregate statistical data for the purpose of obtaining relevant information about the whole. The aggregate or whole of statistical information on a particular character of all the members covered by the investigation…

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ANS:Objective: In this project we are going to clean the dataset of '1985 Automobile Dataset'. About the Dataset: This is a raw dataset about cars in 1985. This data set consists of three types of entities: (a) the specification of an auto in terms of various characteristics, (b) its assigned insurance risk rating, (c)…

QUESTION1 ANS: Similarity Measures for Categorical Data Part 0:Filler Categorical data (also known as nominal data) has been studied for a long time in various contexts. However computing similarity between categorical data instances is not straightforward owing to the fact that there is no explicit notion of ordering…

QUESTION1 ANS: What is Neural Networks? The computing systems inspired by biological neural networks to perform different tasks with a huge amount of data involved is called artificial neural networks or ANN. Different algorithms are used to understand the relationships in a given set of data to produce the best results…

QUESTION2   K- Nearest Neighbors or also known as K-NN belong to the family of supervised machine learning algorithms which means we use labeled (Target Variable) dataset to predict the class of new data point. The K-NN algorithm is a robust classifier which is often used as a benchmark for more complex classifiers…

QUESTIONS1 ANS: Support Vector Machine Pros & Cons 1- Thrives in High Dimension When data has high dimension (think 1000+ to infinity features) a Support Vector Machine with the right settings (right kernel choice etc.) can be the way to go and produce really accurate results.   2- Kernel Flexibility If you're…

L1 vs L2 Regularization: The intuitive difference A lot of people usually get confused which regularization technique is better to avoid overfitting while training a machine learning model. Source — http://laid.delanover.com/difference-between-l1-and-l2-regularization-implementation-and-visualization-in-tensorflow/…

QUESTION:TO calculate the expected value of this probability distribution, we can use the following formula: Expected Value = Σx * P(x) where: x: Data value P(x): Probability of value For example, we would calculate the expected value for this probability distribution to be: Expected Value = 0*0.18  + …

A deep neural network and two machine learning models will be built to tackle the challenge and compare different model performance. Additionally, data sampling techniques will be implemented to improve the model. As normal, split into 9 parts: Business challenge Data review Data processing DNN Model building DNN…

Bi-Directional Recurrent Neural Network: In a bidirectional RNN, we consider 2 separate sequences. One from right to left and the other in the reverse order. But, now comes the question how would you combine both of the RNN’s together. Look at the figure below to get a clear understanding. Bi Directional RNN for…

It works quite similarly for RNNs, but here we’ve got a little bit more going on.   Firstly, information travels through time in RNNs, which means that information from previous time points is used as input for the next time points. Secondly, you can calculate the cost function, or your error, at each time point.…

summary Padding can increase the height and width of the output. This is often used to give the output the same height and width as the input. The stride can reduce the resolution of the output, for example reducing the height and width of the output to only 1/n of the height and width of the input (n is…

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What is Gradient Boosting? Gradient boosting is a technique used in creating models for prediction. The technique is mostly used in regression and classification procedures. Prediction models are often presented as decision trees for choosing the best prediction. Gradient boosting presents model building in stages,…

Bias, Variance, and Regularization in Linear Regression: Lasso, Ridge, and Elastic Net — Differences and uses Regression is an incredibly popular and common machine learning technique. Often the starting point in learning machine learning, linear regression is an intuitive algorithm for easy-to-understand problems.…

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Logistic Regression Gradient Descen The Building Blocks Recall our equation for the Cost Function of a Logistic Regression L(^y,y)=−(ylog^y+(1−y)log(1−^y))L(y^,y)=−(ylog⁡y^+(1−y)log⁡(1−y^)) We use the weights, w, our inputs, x, and a bias term, b to get a vector z.…

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