Data Science Portfolio

Summary : The Repository contains various Applied ML Models (HR Analytics | Banking Analytics | Healthcare Analytics and others) and implementation of the mathematics of various ML Algorithms from scratch in Python.

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1. Applied ML Models

Business Applications and case studies of Machine Learning Models

1.1 HR Analytics - Predicting Employee Turnover

Problem Type: Classification
Algorithms used: Logistic Regression | Random Forest Classifier | Decision Tree Classifier

1.2 Banking Analytics - Bank Fixed Deposit Subscriber Prediction

Problem Type: Classification
Algorithms used: Logistic Regression | Random Forest Classifier | Decision Tree Classifier

1.3 Real Estate Analytics - Housing Price Prediction

Problem Type: Regression
Algorithms used: Linear Regression

1.4 Parking Lot Occupancy Prediction

Problem Type: Classification
Algorithms used: Logistic Regression | Random Forest Classifier | Decision Tree Classifier

1.5 Healthcare Analytics - Predicting Diabetes Rehospitalization

Problem Type: Classification
Algorithms used: Logistic Regression | Random Forest Classifier | Decision Tree Classifier

1.6 Customer Analytics - Predicting Telecom Customer Churn

Problem Type: Classification
Algorithms used: Logistic Regression | Random Forest Classifier | Decision Tree Classifier

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2. ML Models Mathematics from scratch in Python

Implementation of the Mathematical Logic behind various Machine Learning models from scratch in Python

2.1 Linear Regression

Algorithm Type: Regression
Derivation of the cost function - Gradient Descent - Normal Equation - Newton's Method

2.2 Polynomial Regression

Algorithm Type: Regression
Polynomial Regression - Gradient Descent - Newton's Method

2.3 Logistic Regression

Algorithm Type: Regression
Logistic Regression Algorithm using Gradient Descent and Newton's Method

2.4 Mathematical Derivation of the Hessian of Logistic Regression

Calculus - Hessian - Loss Function Derivation

2.5 Gaussian Discriminant Analysis

Algorithm Type: Classification
Gaussian Discriminant Analysis

2.6 Naive Bayes

Algorithm Type: Classification
Bayesian Machine Learning

2.7 K-Means Clustering

Algorithm Type: Clustering
Linear Algebra | Clustering