Statistical Analysis

B.Tech Course Scheme

• B.Tech IT Sem 5 Scheme

• Statistical Analysis Syllabus PDF

Course Materials (PPTs/PDFs/Handouts):

• 1. Introduction to Statistical Analysis

     • Introduction to Statistical Analysis [ PDF ]

  2. Modeling Data

     • Modeling Data [ PDF ]

       • SA Statistical Description of Data [ PDF ]

       • Evaluation and Methodology of Data Science [ PDF ]

       • Population (Probability) Distributions [ PDF ]

       • Solved Examples [ PDF ]

  3. Statistical Inference About One Population

     • Hypothesis Testing (Parametric) [ PDF ]

     • Hypothesis Testing (Non-Parametric) [ PDF ]

     • SA Inferential Statistics: Hypotheses Testing LaTeX PDF

     • SA Solved Examples [ PDF ] 

     • SA Confusion Matrix Examples [ Spreadsheet.xls ]

       • SA Confusion Matrix Example [ PDF ]

       • SA Example: Multiclass Confusion Matrix [ PDF ]

         • Multiclass Confusion Matrix [ Confusion Matrix_Lab.ipynb ]

  4. Statistical Inference About Two Population

     • Statistical Inference About Two Population [ PDF ]

       • 
         

  5. Analysis of Variance

     • Analysis of Variance [ PDF ]

     • 
       

  6. Non-Parametric Statistical Tests

     • Recommender Systems [ PDF ]

     • 
       

💻  💻 💻  💻 💻  💻 💻  💻 💻  💻 💻  💻 

Lab Experiments

• Guidelines for writing Lab Experiments - Jan 2023 [ PDF ]

• SA Lab 1 [ PDF ]

• Anlaysis Example: Test 1 PDF Test 2 PDF

• List of Datasets: 

  • insurance.csv

  • heart.csv

  • iris.csv

  • kerala.csv

1. Introduction to Machine Learning

   • EDA on Insurance Dataset  [  ipython ] [ insurance.csv ]

   • Training Dataset vs Testing Dataset vs Validation Dataset [ ipython ]

   • Implement Machine Learning Steps in Python  [  ipython ]

   • Data Visualization Graph and Plots  [  ipython ]

   • Basics of Python   [  ipython ]

2. Learning with Regression and Trees

   • To implement Linear Regression

   • To implement Logistic Regression 

     • Heart Disease Prediction using Logistic Regression  [  ipython ]

   • Decision Tree Learning

     • Decision Tree Learning using iris Dataset   [  ipython ]

     • Kerala Flood Prediction using Decision Tree   [  ipython ]

   • To implement CART decision tree algorithm

3. Dimensionality Reduction

   • Dimensionality Reduction using PCA [ ipython ]

   • Dimensionality Reduction using SVD [ ipython ]

   • Implementation of Singular Value Decomposition (SVD) [ ipython ]

4. Classification

   • To implement Support Vector Machine

     • Heart Disease Prediction using SVM  [  ipython ]

     • Breast Cancer Prediction using SVM Classifier [ ipython ]

   • To implement Bayesian Classification

   • To implement Hidden Hidden Markov Model

     • Weather Prediction using HMM [ ipython ]

     • Speech recognition using HMM  [ ipython ]

   • To Implement Random Forest

     • Heart Disease Prediction using Random Forest  [  ipython ]

   • To implement K-Nearest Neighbour

     • Heart Disease Prediction k-NN Classifier  [  ipython ]

5. Clustering

   • Density-Based Clustering: DBSCAN [ ipython ]

   • To implement Radial basis functions

6. Applications of Machine Learning

   • Mini project based on any machine learning application

🏁🏁🏁🏁🏁🏁🏁🏁🏁🏁🏁🏁

Lab Manual

• Sample Gray Scale 8-bit BMP Images for experiment [ IMAGE FOLDER ]

• Guidelines for writing DL Lab Experiments - Jan 2023 [ PDF ]

• Machine Learning using Python = Lab Manual - July 2020 [ PDF ]