## Use R to solve these Problems: You just started working for a real estate compan

Use R to solve these Problems:
You just started working for a real estate company and they are looking to make a huge investment into the growing Nashville area. They’ve acquired a dataset about recent sales and want you to build a model to help them accurately find the best value deals when they go to visit next week.
Part 1:
Use proper data cleansing techniques to ensure that you have the highest quality data to model this problem. Detail your process and discuss the decisions you made to clean the data.
Part 2:
Build a linear regression model to accurately predict housing prices and determine what is driving those prices.
Part 3:
Build a decision tree model and compare the results with the results of the previous model.
Part 4:
Build a Random Forest model and compare the results with the results of the previous models.
Part 5:
Build a Gradient Boost model and compare the results with the results of the previous models.
Part 6:
Use multiple benchmarking metrics to compare and contrast the three models. Based on your findings, provide evidence of which model you believe the real estate company should use.
Attach images in the appendix. Code should be provided in an additional document.

## Please read all the associated documentation before requesting the assignment. T

Please read all the associated documentation before requesting the assignment. This is in python and please do not submit a python notebook. This is a neural network assignment that requires a good amount of testing (I don’t expect you to test it all). Please ask plenty of questions before completing this assignment.

## Use R to solve these Problems: You just started working for a real estate compan

Use R to solve these Problems:
You just started working for a real estate company and they are looking to make a huge investment into the growing Nashville area. They’ve acquired a dataset about recent sales and want you to build a model to help them accurately find the best value deals when they go to visit next week.
Part 1:
Use proper data cleansing techniques to ensure that you have the highest quality data to model this problem. Detail your process and discuss the decisions you made to clean the data.
Part 2:
Build a linear regression model to accurately predict housing prices and determine what is driving those prices.
Part 3:
Build a decision tree model and compare the results with the results of the previous model.
Part 4:
Build a Random Forest model and compare the results with the results of the previous models.
Part 5:
Build a Gradient Boost model and compare the results with the results of the previous models.
Part 6:
Use multiple benchmarking metrics to compare and contrast the three models. Based on your findings, provide evidence of which model you believe the real estate company should use.
Attach images in the appendix. Code should be provided in an additional document.

## In this assignment, you will be working in the Jupyter Lab environment to implem

In this assignment, you will be working in the Jupyter Lab environment to implement a nonlinear classifier of 2D points.  As part of your assignment, you will be working on:
Build a classifier model and loss function using TensorFlow API
Use TensorFlow optimizer to perform model parameter tuning
Visualize the classification surface in the 2D space
The entire assignment is done online as a Jupyter notebook worksheet.

## use k-means clustering to find clusters in the data. Evaluate the accuracy, visualize the clusters Complete with hard and soft clustering, like EM, GMM,

use k-means clustering to find clusters in the data. Evaluate the accuracy, visualize the clusters
Complete with hard and soft clustering, like EM, GMM, hierarchial clustering and compare performance with small comment on jupyter notebook. no report required
no need to clean data

https://colab.research.google.com/drive/1gvGBM12RdHQoJG48bguIs2h2MoQ6sxJB?usp=sharing As indicated on the notebook: “In this assignment you have to build and train a multimodal deep neural network for sentiment recognition using tf.keras/pytorch. You have to work with the MOSI dataset, which contains more than 2000 short videos.”
I need the following task to be done: “classification with 2 classes: [-3, +3] values are converted to 2 classes: negative and positive.”. No need to do the optional tasks but please note that the base task that must be done requires to build a MULTIMODAL deep neural network for the classification..