This repository contains implementations of various machine learning models and techniques in Python. Each model addresses a specific business problem and demonstrates practical applications.
- Locally Linear Embedding (LLE)
- Naive Bayes
- Principal Component Analysis (PCA)
- Random Forest Classifier
- Recursive Feature Elimination (RFE)
- Support Vector Machine (SVM)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Agglomerative Clustering
- Gaussian Mixture Models (GMM)
- Isomap
- K-Means Clustering
- Gradient Boosting Regressor
Problem Statement: Reduce the dimensionality of customer data while preserving local relationships for targeted marketing.
Libraries Used:
numpypandasmatplotlibsklearn
Description: LLE is a dimensionality reduction technique that maintains local structures in high-dimensional data. It is useful for visualizing and understanding data in lower dimensions while preserving the relationships between data points.
Problem Statement: Categorize customer reviews into positive or negative sentiments to understand customer satisfaction.
Libraries Used:
numpysklearn
Description: Naive Bayes is a probabilistic classifier based on Bayes' theorem with strong independence assumptions. It is effective for text classification tasks, including sentiment analysis, by predicting the class of text data based on its features.
Problem Statement: Reduce the dimensionality of high-dimensional data to understand key factors influencing customer purchasing behavior.
Libraries Used:
numpypandassklearn
Description: PCA is a technique for dimensionality reduction that transforms data into a lower-dimensional space while retaining as much variance as possible. It helps in identifying the most significant factors influencing the data and simplifying analysis.
Problem Statement: Predict whether a person is likely to purchase a product based on features like age, gender, and estimated salary.
Libraries Used:
numpypandassklearn
Description: Random Forest is an ensemble learning method that constructs multiple decision trees and merges their results to improve classification accuracy. It is effective for handling various types of data and predicting outcomes based on complex feature interactions.
Problem Statement: Identify the most relevant features for predicting employee performance.
Libraries Used:
numpypandassklearn
Description: RFE is a feature selection technique that recursively removes the least important features and builds models with the remaining features. It helps in identifying the most influential features for model performance and reducing overfitting.
Problem Statement: Predict customer churn based on historical data.
Libraries Used:
numpypandassklearn
Description: SVM is a classification method that finds the optimal hyperplane to separate different classes in the data. It is used for binary classification tasks and is effective in high-dimensional spaces.
Problem Statement: Visualize high-dimensional customer purchasing data in a 2D space.
Libraries Used:
numpymatplotlibsklearn
Description: t-SNE is a dimensionality reduction technique that visualizes high-dimensional data by preserving the local similarities in a lower-dimensional space. It is useful for exploring and understanding complex data structures.
Problem Statement: Identify distinct customer segments based on demographic information for targeted marketing campaigns.
Libraries Used:
numpypandasmatplotlibsklearn
Description: Agglomerative Clustering is a hierarchical clustering method that iteratively merges clusters based on similarity. It helps in identifying distinct groups within the data for segmentation and analysis.
Problem Statement: Categorize customers into distinct segments based on purchasing behavior.
Libraries Used:
numpymatplotlibsklearn
Description: GMM is a probabilistic model that assumes the data is generated from a mixture of several Gaussian distributions. It is used for clustering and density estimation, providing a flexible approach to segmenting data.
Problem Statement: Identify patterns and clusters within high-dimensional customer data for marketing strategy optimization.
Libraries Used:
numpymatplotlibsklearn
Description: Isomap is a nonlinear dimensionality reduction technique that maintains global geometric structure by preserving distances between data points. It is useful for visualizing and analyzing complex data relationships.
Problem Statement: Group customers into distinct clusters based on their purchasing behavior.
Libraries Used:
numpypandasmatplotlibsklearn
Description: K-Means Clustering is an iterative algorithm that partitions data into K distinct clusters by minimizing the variance within each cluster. It is widely used for clustering tasks and customer segmentation.
Problem Statement: Predict a continuous target variable based on various input features.
Libraries Used:
numpypandassklearn
Description: Gradient Boosting Regressor is an ensemble learning method that builds multiple weak learners (e.g., decision trees) and combines them to create a strong predictive model. It is effective for regression tasks with complex relationships.
Feel free to explore and use these models for your machine learning tasks. Each implementation includes detailed code examples and explanations to help you understand and apply these techniques.