Learn · Predictive Analytics - Regression and Classification
Lecture 58 of 63Week 10
Hands on with R: Dynamic Pricing with Cheese Data
Prof. Sourish Das · Chennai Mathematical Institute
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- From
- Predictive Analytics - Regression and Classification, Chennai Mathematical Institute (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge. Coordinated by IIT Madras.
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- Tweens profiles and older. How courses are placed
All the lectures
- Introduction
- Least Squares method
- Hands-on with Python Part -1
- Hands-on with R Part -1
- Categorical Variable as Predictor Part -1
- Categorical Variable as Predictor Part -2
- Hands-on with R Part -2
- Understanding the joint probability from data perspective
- Hands-on with R Part -3
- Regression Line as Conditional Expectation
- Normal Equations
- Gauss Markov Theorem
- Hands-on with Python Part -2
- Geometry of Regression Model and Feature Engineering
- Sampling Distribution and Statistical Inference of Regression Coefficient
- Hands-on with R Part -4
- Checking Model Assumptions
- Comparing Models with Predictive Accuracy
- Hands-on with Julia
- Model Complexity, Bias and Variance Tradeoff
- Feature Selection, Variable Selection
- Hands on with R Part - 5
- Understanding Multicollinearity
- Ill-Posed Problem and Regularisation, LASSO and Risdge
- Time Series Forecasting with Regression Model
- Hands on with R Part - 6
- Granger Causal model
- Hands on with R Part - 7
- Capital Asset Pricing Model
- Hands on with R for CAPM
- Bootstrap Regression
- Hands on with R for Bootstrap Regression
- Hands on with Python : Handle multicollinearity with Ridge correction
- Hands on with Julia: Implemente Chennai Temperature Analysis with Julia and CRRao
- Introduction to logistic Regression
- Maximum Likelihood Estimate for Logistic Regression
- Hands on with R for Logistic Regression
- Hands on with R : Measure Time performance of R code
- Statistical Inference of Logistic Regression
- Hands on with R with Iris Dataset
- Multi-Class Classification with Discriminant Analysis
- Hands on with R: Implement LDA
- Effect of Feature Engineer in Logistic Regression
- Logistic Regression to Deep Learning Neural Network
- Hands on with R : Feature Engineer in Logistic Regression
- Generalised Linear Model
- Hands on with R : Poisson Regression with Football Data
- Gaussian Process Regression
- Hands on with R: Implement GP Regression from scratch
- Tree Structured Regression
- Hands on with R: Implement Tree Regression and Random Forest with Simulated Data
- Hands on with R: Implement Tree Regression and Random Forest with EPL football Data
- Hands on with Python : Analysis of Bangalore House Price Data
- Hands on with R : Prediction of Bangalore House Price
- Hands on with R : More Prediction of Bangalore House Price
- Hands on with R: Some Correction with Bangalore House Price Data Prediction
- Hands on with R: Classify fake bank note with GLM
- Hands on with R: Dynamic Pricing with Cheese Data
- Hands on with Julia | Bayesian Logistic Regression with Horse Shoe Prior | Genetic Data Analysis
- Hands on with Julia | Bayesian Poisson Regression with Horse Shoe Prior | English Premier League Data
- Why Julia is Future for Data Science Projects?
- Course Review
- Concluding Remarks