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A complete Brush-up of all models through mini-Projects |
This folder consists of the notebooks and datasets and step by step process of ML model applications
- Linear Regression Single-variable - Given the data of per-capita income of previous years build a machine learning model that can predict per-capita income for a given year
- Linear Regression Multi-Variate - Predict the salary of an employee given his experience, test score and interview score
- Gradient Descent computation - Comput and visualize Gradient descent
- Pickle and joblib - Saving a trained model using pickle and joblib
- One Hot Encoding - Steps to encode a multi-class label using Dummies and One Hot Encoding
- Splitting Data using train_test_split - Given a dataset containing prices of used BMW cars, analyze it and build a prediction function that can predict a price by taking the mileage and age of the car as input. We will use sklearn train_test_split method to split the training and testing dataset
- Logistic Regression - Given an HR analytics dataset consisting of satisfaction level, salary, etc. predict if the employee is likely to quit or not
- Logistic Regression - multi-class and Confusion matrix plotting - MNIST dataset classification using Logistic regression
- Decision Tree - Survival prediction on Titanic Dataset
- Random Forest - Given IOWA property sale data predict the sale price of a property
- Support Vector Machine - Classify digits using pixel values from MNIST dataset using SVM
- K-fold Cross Validation - Perform k-fold validation on IRIS dataset using Random Forest Classifiers
- K-means - Perform k-means clustering on income dataset
- Naive Bayes - Spam detection using Naive Bayes
- GridsearchCV - Hypertune SVM model for IRIS dataset classification using GridSearch
References: codebasics exercises and kaggle