Machine Learning Assignments of inuroun academy ML with master deployment and deep learning 29th aug. 2020
Build the linear regression model using scikit learn in boston data to predict 'Price' based on other dependent variable.
I decided to treat this as a classification problem by creating a new binary variable affair (did the woman have at least one affair?) and trying to predict the classification for each woman.
In this assignment, students will be using the K-nearest neighbors algorithm to predict how many points NBA players scored in the 2013-2014 season. A look at the data Before we dive into the algorithm, let’s take a look at our data. Each row in the data contains information on how a player performed in the 2013-2014 NBA season.
Predicting Survival in the Titanic Data Set We will be using a decision tree to make predictions about the Titanic data set from Kaggle. This data set provides information on the Titanic passengers and can be used to predict whether a passenger survived or not.
In this assignment students need to predict whether a person makes over 50K per year or not from classic adult dataset using XGBoost. The description of the dataset is as follows:
In this assignment students will build the random forest model after normalizing the variable to house pricing from boston data set.
Problem 1: Prediction task is to determine whether a person makes over 50K a year.
Problem 2: Which factors are important
Problem 3: Which algorithms are best for this dataset
In this assignment students have to compress racoon grey scale image into 5 clusters. In the end, visualize both raw and compressed image and look for quality difference.
The raw image is available in spicy.misc package with the name face.
Hint:
import numpy as np from sklearn import cluster, datasets from scipy import misc
Problem 1: There are various stocks for which we have collected a data set, which all stocks are apparently similar in performance
Problem 2: How many Unique patterns that exist in the historical stock data set, based on fluctuations in price.
Problem 3: Identify which all stocks are moving together and which all stocks are different from each other.
In this project you are going to predict the overall rating of soccer player based on their attributes such as 'crossing', 'finishing etc.
The dataset you are going to use is from European Soccer Database (https://www.kaggle.com/hugomathien/soccer) has more than 25,000 matches and more than 10,000 players for European professional soccer seasons from 2008 to 2016.