Exploratory Data Analysis, Feature Engineering & Hypothesis
Learn exploratory data analysis, feature engineering & hypothesis testing through Telco churn case study. Step-by-step Python guide for data science beginners.
Customer Churn Prediction With Random Forest and XGBoost
Better customer churn prediction is possible: See how we applied both Random Forest and XGBoost models to telecom data to anticipate cancellations in advance.
Decision trees explained through the Titanic dataset
A beginner-friendly guide to using decision trees for predicting Titanic survival, featuring step-by-step code, clear explanations, pruning, and evaluation.
Underfitting and Overfitting with Polynomial Regression
Learn how underfitting and overfitting affect model performance using polynomial regression on real housing data, with clear visuals and code examples.
Neural Network from Scratch with NumPy for MNIST Digits
This project walks through creating a neural network using NumPy to recognize handwritten digits. Gain hands-on experience with forward and backpropagation.
Logistic Regression for Email Spam Detection with NumPy
Build a spam detection model using logistic regression and NumPy. Learn how to process text data, apply the sigmoid function, and classify emails effectively.
Linear Regression With NumPy: House Price Prediction
Predict house prices using a linear regression model built entirely with NumPy. This beginner project covers data prep, cost function, and gradient descent.
