Supervised Learning - Prediction Week 3 Challenge

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The project files are attached below

here I have attached the drive link for the worksheet. https://docs.google.com/spreadsheets/d/1jtXNTtA1mmR9ZlWx8VNU2ck2_-2oQl5H/edit?usp=sharing&ouid=111373955071462287585&rtpof=true&sd=true    3b) VBA Code to convert selected data into pdf from applicant data: Sub PDFconversion()Dim r As RangeDim…

The file is attached all mentioned tasks on the provided dataset are done and the formatted file is attached.

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About Dataset Context Dataset is about cars from back in 85. It's raw and messy. Content 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) its normalized losses in use as compared to other cars. The second rating…

Answer 1: The simplest way to find similarity between two categorical attributes is to assign a similarity of 1 if the values are identical and similarity of 0 if the values are not identical. Another method involves converting the categorical data into label encoding using for loop which runs for the length of the…

Answer 1: Neural Networks Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and deep learning.   What are neural networks? Neural networks, also known as artificial neural networks (ANNs) or simulated…

Answer 1: pros: Pros: No assumptions about data — useful, for example, for nonlinear data Simple algorithm — to explain and understand/interpret High accuracy (relatively) — it is pretty high but not competitive in comparison to better supervised learning models Versatile — useful for classification…

Answer: PROS: 1- Thrives in High Dimension When data has a 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 a hands-on person who likes to learn and understand…

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project 1 is completed and the file is attached below

Q1. Perform Gradient Descent in Python with any loss function Answer: In machine learning, the goal is to predict the target variable as close to the ground truth as possible. Thus, the model we adopt for prediction should have reasonable accuracy. As the input values are fixed, to improve the quality of the…

for question 1 file is attached Q.2)What is the significance of the expected value when a simple mean (Sum of all observations/number of observations) is already in place? Answer: An expected value gives a quick insight into the behavior of a random variable without knowing if it is discrete or continuous. Therefore, two…

Q1. Answer: The formula for sample variance is given as S²=∑(xi−μ)²/n−1 The formula for sample variance is given as σ²=∑(xi−μ)²/n   To put it simply (n−1) is a smaller number than (n). When you divide by a smaller number you get a larger number.…

file attached  for question 1,2,3 see images

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file attached of  Project 2 - EDA on Vehicle Insurance Customer Data