Machine Learning Projects

The dataset: Your dataset appml-assignment1-dataset.pkl contains :

• historical exchange rates between various currencies and the US dollar on a major trading platform (precisely midpoints between the bid and ask)

• during the trading day trading prices for exchange traded funds that attempt to track stock markets in the associated countries

• an index that tracks a collection of US treasuries, and its volatility, and the S&P 500 index

For each such index/ETF/exchange rate, each of the following are provided hourly

Decision trees can handle both categorical and numerical data. They are used for classification and regression problems. They can handle missing data pretty well, too!

The algorithms for building trees breaks down a data set into smaller and smaller subsets while an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes.

Collect the data

In this, we implement it with the help of Banknote Case Study

You can collect the data from here(Banknote data)

We shall be covering the role of unsupervised learning algorithms, their applications, and the K-means clustering approach.

Machine learning algorithms can be classified as Supervised and unsupervised learning.

In supervised learning, there will be the data set with input features and the target variable. The aim of the algorithm is to learn the dataset, find the hidden patterns in it and predict the target variable. The target variable can be continuous as in the case of Regression or discrete as in the case of Classification.

Examples of Regression problems include housing price prediction, Stock market prediction, Air humidity, and temperature prediction. Examples of classification problems include Cancer prediction(either benign or malignant), email spam classification, etc.

Other areas of machine learning is unsupervised learning, where we will have the data, but we don’t have any target variable as in the case of supervised learning. So the goal here is to observe…