Machine Learning and Data Science Overview

Machine Learning Overview

• Machine learning is a broad field focused on developing algorithms that allow computer systems to learn from data without explicit programming.
• Data science involves a process for working with data which includes collecting, processing and cleaning data.
• The process culminates in creating data products.

The Data Science Process

• Data collection is the initial step in the data science process.
• Data processing is used to convert raw data into a structured, usable format.
• Data cleaning identifies and corrects errors or inconsistencies found in the data.
• Exploratory data analysis (EDA) aims to understand patterns, relationships, and interesting characteristics of data sets.
• Machine learning algorithms and statistical modeling build and train models to learn from data.
• Communication & visualization helps present findings in a suitable format for decision-making and understanding.
• Make decisions are a micro level data strategy.

Classification Algorithms

• Naive Bayes classifier is a simple algorithm based on Bayes' theorem.
• Support vector machines (SVMs) find optimal hyperplanes to separate data points.
• K-nearest neighbor (k-NN) assigns data points based on their proximity to existing data points.
• Random forest trees use ensemble learning by combining multiple decision tree models.
• Decision trees model data by recursively partitioning it into smaller subgroups based off of pre-existing attributes.
• Logistic regression is a statistical model for binary classification tasks.

Clustering Algorithms

• K-means clustering is a popular method for partitioning data points into distinct clusters.
• Mean Shift Clustering Algorithm is another method for grouping similar data points together.
• Gaussian Mixture Model is a probabilistic model for clustering, based on density estimation.

Feature Selection

• Feature selection involves selecting a subset of relevant features from the original features.
• This is important for avoiding the curse of dimensionality.
• The selection process is done according to a certain criterion.

Decision Trees

• Decision trees are tree-like models for classification or regression.
• They involve testing attributes and branching out accordingly.
• They can be used as rules.

Example Data - Tennis Play

• The example demonstrates a dataset for predicting tennis playing conditions.
• Factors like Outlook, Temperature, Humidity, and Wind are tested.
• The ultimate goal is to predict whether a player will play tennis on a given day.

Decision Tree Hypothesis Space

• Internal nodes check attribute values, branching based on results.
• Leaf nodes represent a class outcome.
• Irrelevant attributes can be identified during modeling. For example, temperature would not be helpful in determining whether someone will plays tennis or not.

Homework - Attractive Person

• Students need to identify the most important attribute to determine attractiveness based on data.

Python Libraries & Code

• Libraries such as pandas for data handling, sklearn for modeling, and scikit-learn for metrics will aid analysis.
• Specific code examples (e.g., loading libraries, data loading, model building) will aid data visualization and model evaluation.

Pima Indian Diabetes Dataset Example

• Pima Indian Diabetes dataset is a CSV file for analysis, containing features and a target of either diabetic or not.
• This dataset includes attributes such as pregnant, glucose, blood pressure etc.
• The dataset is used to evaluate performance of models.

Model Evaluation

• Accuracy measures how frequently the model correctly classifies data points.
• In the diabetes model, the accuracy score was around 67.5%