Teaching: UCS654 - Predictive Analytics using Statistics

UCS654: Predictive Analytics using Statistics (Jan to June 25 - Even 2425)

Table of Content

Join WhatsApp Group | Click Here

01 - Lecture Resources
02 - Lab Experiments
03 - Assignments
04 - Kaggle Hack

Marking Scheme

MST = 35 Marks
EST = 35 Marks
Sessionals = 30 marks
- Quiz = 10 marks
- Guided Projects = 10 marks
- Kaggle Hack = 10 marks

01 - Lecture Resources

Topic01 - Topsis - Multiple Criteria Decision Making | Link
Topic02 - Sampaling | Link
Topic03 - Distribution | Link
Topic04 - Machine Learning using Pycaret | Link
Topic05 - Data Generation using Modelling and Simulation for Machine Learning | Link
Topic06 - Association Mining - Apriori | Link
Topic07 - Association Mining - ECLAT | Link
Topic08 - Multi-Threading using Python | Link
Topic09 - Hypothesis Testing and Parameter Estimation | Link
Topic10 - Parameter Optimization | Link
Topic11 - Nonliner Modelling | Link
Topic12 - Measuring Data Similarity and Dissimilarity | Link
Topic13 - Ensemble Technique | Link

02 - Labs Experiments

02 - Labs Experiments - Guided Project & Kaggle

General Instructions

1. Guided Project (GP)

Step 1: Create a login with Thapar email id on Coursera (coursera.org).
Step 2: Complete the given below "Guided Project" and submit the "Compeletion Certificate" link.
Step 3: Explore Sample Certificate | Click Here
Step 4: Explore all "Guided Projects" on Coursera | Click Here
Maximum marks for maximum submissions

2. Kaggle Resources

Five days short and intensive course on Kaggle (1hr each; total = 5hr) | Click Here
Kaggle Grand Master Talks

T-1 | T-2 | T-3 | T-4 | T-5 | T-6 | T-7 | T-8 | T-9 | T-10

Getting started with Kaggle by Mr. Raghav Garg, Mr. Aadil Garg, Mr. Pratham Garg | Click Here
Kaggle Resources by Mr. Eishkaran Singh | Click Here
Kaggle notebooks
- Latest Playground Series Basic's Notebook for Beginners | Click Here
- Latest Playground Series Advanced Approach | Click Here
- XGB HyperParameter Tuning Notebook | Click Here

3. Students Achievement @ Kaggle | Click Here

Lab01 | Due Date: 13-01-2025 07:59:59
1. Basics of R - Part 1
  - Install R and R Studio and Practice "Basics of R" | Link
2. Guided Project-01 | Submission Link
  - Predict Future Product Prices Using Facebook Prophet
    OR
  - Choose any other guided project | Click Here
3. Solve n Kaggle Problems [n=2,3,4....] | Submission Link

Lab02 | Due Date: 20-01-2025 07:59:59
1. Basics of R - Part 2
  - Practice Chapter 5 and Chapter 6 from "05 - R for Everyone - Advanced Analytics and Graphics" book | Link
2. Guided Project-02 | Submission Link
  - Building Custom Regional Reports with Google Analytics
    OR
  - Choose any other guided project | Click Here
3. Solve n Kaggle Problems [n=2,3,4....] | Submission Link

Lab03 | Due Date: 27-01-2025 07:59:59
1. Basics of R - Part 3
  - Explore and Practice Chapter 6, 7, 8, 9, and 10 from "05 - R for Everyone" book | Link
2. Guided Project-03 | Submission Link
  - Analyze Box Office Data with Plotly and Python
    OR
  - Choose any other guided project | Click Here
3. Solve n Kaggle Problems [n=2,3,4....] | Submission Link

Lab04 | Due Date: 03-02-2025 07:59:59
1. Basics of R - Part 4
  - Practice Chapter 11, 12, and 13 from "05 - R for Everyone" book | Link
2. Guided Project-04 | Submission Link
  - Analyze Stock Data using R and Quantmod Package
    OR
  - Choose any other guided project | Click Here
3. Solve n Kaggle Problems [n=2,3,4....] | Submission Link

Lab05 | Due Date: 10-02-2025 07:59:59
1. Advance of R - Part 1
  - Practice Chapter 14, 15, and 16 from "05 - R for Everyone" book | Link
2. Guided Project-05 | Submission Link
  - Create Interactive Dashboards with Streamlit and Python
    OR
  - Choose any other guided project | Click Here
3. Solve n Kaggle Problems [n=2,3,4....] | Submission Link

Lab06 | Due Date: 17-02-2025 07:59:59
1. Advance of R - Part 2
  - Practice Chapter 17, and 18 from "05 - R for Everyone" book | Link
2. Guided Project-06 | Submission Link
  - Data Visualization with Plotly Express
    OR
  - Choose any other guided project | Click Here
3. Solve n Kaggle Problems [n=2,3,4....] | Submission Link

Lab07 | Due Date: 24-02-2025 07:59:59
1. Advance of R - Part 3
  - Practice Chapter 19, 20, 21, 22, and 23 from "05 - R for Everyone" book | Link
2. Guided Project-07 | Submission Link
  - Build Data Analysis tools using R and DPLYR
    OR
  - Choose any other guided project | Click Here
3. Solve n Kaggle Problems [n=2,3,4....] | Submission Link

Lab08 | Due Date: 24-03-2025 07:59:59
1. Advance of R - Part 4
  - Practice Chapter 24, 25, and 26 from "05 - R for Everyone" book | Link
2. Guided Project-08 | Submission Link
  - Create Technical Stock Charts Using R and Quantmod
    OR
  - Choose any other guided project | Click Here
3. Solve n Kaggle Problems [n=2,3,4....] | Submission Link

Lab09 | Due Date: 31-03-2025 07:59:59
1. Advance of R - Part 5
  - Practice Chapter 30 from "05 - R for Everyone" book | Link
2. Guided Project-09 | Submission Link
  - Visualizing Citibike Trips with Tableau
    OR
  - Choose any other guided project | Click Here
3. Solve n Kaggle Problems [n=2,3,4....] | Submission Link

Lab10 | Due Date: 07-04-2025 07:59:59
1. Advance of R - Part 6
  - Practice "Descriptive statistics in R" | Link
2. Guided Project-10 | Submission Link
  - Principal Component Analysis with NumPy
    OR
  - Choose any other guided project | Click Here
3. Solve n Kaggle Problems [n=2,3,4....] | Submission Link

Lab11 | Due Date: 14-04-2025 07:59:59
1. Choose any other guided project | Click Here | Submission Link
2. Solve n Kaggle Problems [n=2,3,4....] | Submission Link

Lab12 | Due Date: 21-04-2025 07:59:59
1. Choose any other guided project | Click Here | Submission Link
2. Solve n Kaggle Problems [n=2,3,4....] | Submission Link

Lab13 | Due Date: 28-04-2025 07:59:59
1. Choose any other guided project | Click Here | Submission Link
2. Solve n Kaggle Problems [n=2,3,4....] | Submission Link

Lab14 | Due Date: 05-05-2025 07:59:59
1. Choose any other guided project | Click Here | Submission Link
2. Solve n Kaggle Problems [n=2,3,4....] | Submission Link

Lab15 | Due Date: 12-05-2025 07:59:59
1. Choose any other guided project | Click Here | Submission Link
2. Solve n Kaggle Problems [n=2,3,4....] | Submission Link

03 - Assignments

Assignment01 - Topsis
- Due Date: For C21-75, Q11-25 : 20 Jan 2025 | 07:59:59
- Due Date: For C11-15, P1: 27 Jan 2024 | 07:59:59
- Assignment Link | Submission Link
Assignment02 - Sampling
- Due Date: For C21-75, Q11-25 : 27 Jan 2025 | 07:59:59
- Due Date: For C11-15, P1: 20 Jan 2024 | 07:59:59
- Assignment Link | Submission Link
Assignment03 - Topsis for Pretrained Models
- Due Date: For COE 1-21: 29 Jan 2024 | 07:59:59
- Due Date: For COE 22-30 and CSE 1-12: 04 Feb 2024 | 07:59:59
- Assignment Link | Submission Link
Assignment04 - Clustering
- Due Date: For COE 1-21: 12 Feb 2024 | 07:59:59
- Due Date: For COE 22-30 and CSE 1-12: 19 Feb 2024 | 07:59:59
- Assignment Link | Submission Link
Assignment05 - Mashup
- Due Date: For COE 1-21: 4 March 2024 | 07:59:59
- Due Date: For COE 22-30 and CSE 1-12: 4 March 2024 | 07:59:59
- Assignment Link | Submission Link
Assignment06 - Parameter Estimation
- Due Date: For COE 1-21: 14 April 2024 | 07:59:59
- Due Date: For COE 22-30 and CSE 1-12: 21 April 2024 | 07:59:59
- Assignment Link | Submission Link
Assignment07 - Multi Threading
- Due Date: For COE 1-21: 21 April 2024 | 07:59:59
- Due Date: For COE 22-30 and CSE 1-12: 14 April 2024 | 07:59:59
- Assignment Link | Submission Link
Assignment08 - Parameter Optimization of SVM
- Due Date: For COE 1-21: 25 April 2024 | 07:59:59
- Due Date: For COE 22-30 and CSE 1-12: 21 April 2024 | 07:59:59
- Assignment Link | Submission Link

Assignment01 - Marks Analysis | 05 Marks | Due Date: 30 Jan 2022 | 23:59:59 | Assignment Link | Submission Link

Assignent02 - Feature Extraction | 05 Marks | Due Date: 06 Feb 2022 | 23:59:59 | Assignment Link | Submission Link

Assignment03 - Classification | 05 Marks | Due Date: 20 Feb 2022 | 23:59:59 | Assignment Link | Submission Link

Assignment05 - Google Data Studio | 05 Marks | Due Date: 10 March 2022 | 23:59:59 | Assignment Link | Submission Link

04-KaggleHack

04 - Kaggle Hack

Kaggle-Hack-Lab-Exam-1 | Due Date: 20 Jan 2025 07:59:59 | Click Here to participate
Kaggle-Hack-Lab-Exam-2 | Due Date: 27 Jan 2025 07:59:59 | Click Here to participate
Kaggle-Hack-Lab-Exam-3 | Due Date: 03 Feb 2025 07:59:59 | Click Here to participate
Kaggle-Hack-Lab-Exam-4 | Due Date: 10 Jan 2025 07:59:59 | Click Here to participate
Kaggle-Hack-Lab-Exam-5 | Due Date: 17 Jan 2025 07:59:59 | Click Here to participate

UCS654: Predictive Analytics using Statistics (Jan to June 25 - Even 2425)

Table of Content

Join WhatsApp Group | Click Here

01 - Lecture Resources 02 - Lab Experiments 03 - Assignments 04 - Kaggle Hack

Marking Scheme

MST = 35 Marks

EST = 35 Marks

Sessionals = 30 marks

Quiz = 10 marks

Guided Projects = 10 marks

Kaggle Hack = 10 marks

01 - Lecture Resources

​

Topic01 - Topsis - Multiple Criteria Decision Making | Link

Topic02 - Sampaling | Link

Topic03 - Distribution | Link

Topic04 - Machine Learning using Pycaret | Link

Topic05 - Data Generation using Modelling and Simulation for Machine Learning | Link

Topic06 - Association Mining - Apriori | Link

Topic07 - Association Mining - ECLAT | Link

Topic08 - Multi-Threading using Python | Link

Topic09 - Hypothesis Testing and Parameter Estimation | Link

Topic10 - Parameter Optimization | Link

Topic11 - Nonliner Modelling | Link

Topic12 - Measuring Data Similarity and Dissimilarity | Link

Topic13 - Ensemble Technique | Link

02 - Labs Experiments - Guided Project & Kaggle

General Instructions

​

1. Guided Project (GP)

Step 1: Create a login with Thapar email id on Coursera (coursera.org).

Step 2: Complete the given below "Guided Project" and submit the "Compeletion Certificate" link.

Step 3: Explore Sample Certificate | Click Here

Step 4: Explore all "Guided Projects" on Coursera | Click Here

Maximum marks for maximum submissions

​

2. Kaggle Resources

Five days short and intensive course on Kaggle (1hr each; total = 5hr) | Click Here

Kaggle Grand Master Talks

T-1 | T-2 | T-3 | T-4 | T-5 | T-6 | T-7 | T-8 | T-9 | T-10

Getting started with Kaggle by Mr. Raghav Garg, Mr. Aadil Garg, Mr. Pratham Garg | Click Here

Kaggle Resources by Mr. Eishkaran Singh | Click Here

Kaggle notebooks

Latest Playground Series Basic's Notebook for Beginners | Click Here

Latest Playground Series Advanced Approach | Click Here

XGB HyperParameter Tuning Notebook | Click Here

3. Students Achievement @ Kaggle | Click Here

​

​

Lab01 | Due Date: 13-01-2025 07:59:59

Basics of R - Part 1

Install R and R Studio and Practice "Basics of R" | Link

Guided Project-01 | Submission Link

Predict Future Product Prices Using Facebook Prophet OR

Choose any other guided project | Click Here

Solve n Kaggle Problems [n=2,3,4....] | Submission Link

​

Lab02 | Due Date: 20-01-2025 07:59:59

Basics of R - Part 2

Practice Chapter 5 and Chapter 6 from "05 - R for Everyone - Advanced Analytics and Graphics" book | Link

Guided Project-02 | Submission Link

Building Custom Regional Reports with Google Analytics OR

Choose any other guided project | Click Here

Solve n Kaggle Problems [n=2,3,4....] | Submission Link

​

Lab03 | Due Date: 27-01-2025 07:59:59

Basics of R - Part 3

Explore and Practice Chapter 6, 7, 8, 9, and 10 from "05 - R for Everyone" book | Link

Guided Project-03 | Submission Link

Analyze Box Office Data with Plotly and Python OR

Choose any other guided project | Click Here

Solve n Kaggle Problems [n=2,3,4....] | Submission Link

​

Lab04 | Due Date: 03-02-2025 07:59:59

Basics of R - Part 4

Practice Chapter 11, 12, and 13 from "05 - R for Everyone" book | Link

Guided Project-04 | Submission Link

Analyze Stock Data using R and Quantmod Package OR

Choose any other guided project | Click Here

Solve n Kaggle Problems [n=2,3,4....] | Submission Link

01 - Lecture Resources
02 - Lab Experiments
03 - Assignments
04 - Kaggle Hack

Predict Future Product Prices Using Facebook Prophet
OR

Building Custom Regional Reports with Google Analytics
OR

Analyze Box Office Data with Plotly and Python
OR

Analyze Stock Data using R and Quantmod Package
OR

Create Interactive Dashboards with Streamlit and Python
OR

Data Visualization with Plotly Express
OR

Build Data Analysis tools using R and DPLYR
OR

Create Technical Stock Charts Using R and Quantmod
OR

Visualizing Citibike Trips with Tableau
OR

Principal Component Analysis with NumPy
OR