Welcome to this Machine Learning repository! This collection contains practical projects and examples designed to help you understand and apply core machine learning concepts and techniques.
- Basic to Advanced Algorithms: Explore a range of algorithms including logistic regression, decision trees, clustering, and ensemble methods.
- Clean, Well-Documented Code: Each project includes clear code with comments to guide you through the implementation.
- Real-World Datasets: Work with real datasets to gain hands-on experience. or learn how to generate random data to work with it
- Step-by-Step Tutorials: Follow along with tutorials that explain concepts and walk you through the coding process.
- Beginners eager to learn machine learning fundamentals.
- Enthusiasts looking to build and expand their portfolio.
- Developers interested in practical applications of ML in Python.
To get started, clone this repository and explore the folder for different projects. Each Python file contains specific instructions and explanations.
What is a Decision Tree? A Decision Tree is a supervised machine learning model that works like a flowchart or a tree structure to make decisions based on input data. It splits data into branches based on feature values, leading to decisions (leaf nodes) at the end.
Each node represents a feature (e.g., Rank, Age).
Each branch represents a decision rule (e.g., Rank <= 6.5).
Each leaf represents an outcome (e.g., Go = YES or NO).
The tree is built by selecting the best feature splits that separate the data into groups with similar outcomes, using criteria like the Gini impurity to measure the quality of splits.
How the Code Works Data Preparation: Converts categorical data (Nationality and Go) into numeric values because decision trees require numeric inputs.
Feature Selection: Uses columns like Age, Experience, Rank, and Nationality as inputs to predict the target variable 'Go'.
Model Training: Fits a decision tree classifier on the data, learning decision rules from the features.
Visualization: Plots the decision tree showing how decisions are made at each node.
Prediction: Uses the trained tree to predict whether the person would go to a comedy show given new comedian features.
Additional Notes on Decision Trees The Gini impurity measures how mixed the classes are in a node; lower values mean purer splits.
Decision trees can sometimes overfit the training data if too deep.
Predictions are probabilistic and can vary slightly if the tree is re-trained.
Decision trees are intuitive and easy to visualize, making them useful for explaining decisions.
Hierarchical Clustering :
numpy: Used for numerical operations and managing arrays.
matplotlib: For plotting scatter plots and dendrograms.
scipy.cluster.hierarchy: Provides linkage to perform hierarchical clustering and dendrogram to visualize the cluster hierarchy.
scikit-learn: Provides AgglomerativeClustering for an easy-to-use hierarchical clustering implementation.
Install missing packages via: pip install numpy matplotlib scipy scikit-learn
What is Hierarchical Clustering? Hierarchical clustering is an unsupervised learning technique used to group similar data points into clusters without needing labeled data or a target variable.
It builds a hierarchy (tree) of clusters, represented visually as a dendrogram.
The most common approach is Agglomerative Clustering (bottom-up):
Start with each data point as its own cluster.
Iteratively merge the two closest clusters based on a distance metric (e.g., Euclidean distance).
Continue merging until all points form a single cluster or until a desired number of clusters is reached.
The Ward linkage method is used here, which merges clusters to minimize the variance within clusters, producing compact clusters.
How the Code Works Data Preparation: Creates a small dataset of 10 points in 2D space.
Visualization: Plots the raw data points to understand their distribution.
Linkage Calculation: Uses SciPy’s linkage function to compute hierarchical clustering based on Ward linkage and Euclidean distance.
Dendrogram Plot: Visualizes the clustering hierarchy, showing how clusters merge at different distances.
Clustering with scikit-learn: Uses AgglomerativeClustering to assign each data point to one of two clusters.
Cluster Visualization: Colors points by their cluster assignment to show the grouping.