Machine learning allows computers to learn and improve from experience, discover patterns in data, automate tasks, and create intelligent systems that adapt and improve over time. It's essential for making sense of large datasets and has the potential to free up human workers for more creative work.

Supervised learning is a type of machine learning where the computer is trained on labeled data to make accurate predictions or decisions when presented with new, unlabeled data.

Classification in machine learning is like sorting objects into different categories based on their features. The computer is trained on labeled data, which is used to create a model that can classify new, unlabeled data into one of several predefined categories.

✅ Day-1: Advertisement Sale prediction from an existing customer using LOGISTIC REGRESSION

✅ Day-2: Salary Estimation using K-NEAREST NEIGHBOR

✅ Day-3: Character Recognition using SUPPORT VECTOR MACHINE

✅ Day-4: Titanic Survival Prediction using NAIVE BAYES

✅ Day-5: Leaf Detection using DECISION TREE

✅ Day-6: Handwritten digit recognition using RANDOM FOREST

🔯 Day-7: Evaluating Classification model Performance using:

• CONFUSION MATRIX
• CAP CURVE ANALYSIS
• ACCURACY PARADOX

🔯 Day-8: Classification Model Selection for Breast Cancer classification

Regression in machine learning is like finding the mathematical relationship between two or more variables, so we can predict a numerical output based on the input data. It's like teaching a computer to recognize patterns in data and make predictions based on those patterns.

✅ Day-9: House Price Prediction using LINEAR REGRESSION Single Variable

✅ Day-10: Exam Mark Prediction using LINEAR REGRESSION Multiple Variable

✅ Day-11: Predicting the Previous salary of the New Employee using POLYNOMIAL REGRESSION

✅ Day-12: Stock price prediction using SUPPORT VECTOR REGRESSION

✅ Day-13: Height Prediction from the Age using DECISION TREE REGRESSION

✅ Day-14: Car price prediction using RANDOM FOREST REGRESSION

🔯 Day-15: Evaluating Regression model performance using:

• R-SQUARED INTUITION
• ADJUSTED R-SQUARED INTUITION

🔯 Day-16: Regression Model Selection for Engine Energy prediction.

Unsupervised learning is like exploring an unknown territory without a map or guide. The computer is given unlabeled data and tasked with finding patterns or relationships on its own, without prior knowledge of the categories or labels. It's like discovering hidden structures or insights in data that would be difficult or impossible for a human to find.

✅ Day-17: Income Spent Clustering using K-Means. ✅ Day-18: Customer Spending analysis using HIERARCHICAL CLUSTERING ✅ Day-19: Clustering Plant Iris using Principal Component Analysis

  Language: Python
  Knowledge Area: Machine Learning
  Libraries: pandas, numpy, matplotlib, sklearn

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✨🤝 Pantech Solutions Internship Machine Learning Concept