Fall 2024: Data Mining Lab

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Instructions

  • Please be on time to avoid the Attendance Penalty.
  • Please sign on the Attendance Register before your take a seat.
  • Please put your mobile phone in the Silent Mode.
  • Each lab assignment needs to be submitted in the Google Classroom for evaluation(will be notified in the GC lab-wise, submit before the deadline).
  • Turn off(shut down) your assigned computer and arrange the chair before you leave the lab.

Guidelines

Lab 0: Getting Started ( week of 05th & 12th August 2024 )

Q. NO. Program Practical No. Remarks
1 https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial1/tutorial1.html Practice Set No. 1 Introduction to Python
2 https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial2/tutorial2.html Practice Set No. 2 Introduction to Numpy and Pandas
3 https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial3/tutorial3.html Practice Set No. 3 Data Exploration

Lab 1: ( week of 19th & 26th August 2024 )

Q. NO. Program Practical No. Remarks
1 Apply data cleaning techniques on any dataset (e.g. Chronic Kidney Disease dataset from UCI repository). Techniques may include handling missing values, outliers and inconsistent values. Also, a set of validation rules may be specified for the particular dataset and validation checks performed. Practical No. 1 Dataset: kidneyDisease.csv

Download from Kaggle: Chronic KIdney Disease dataset
Tutorial: Tutorial on Handling Missing values

Lab 2: ( week of 2nd & 9th September 2024 )

Q. NO. Program Practical No. Remarks
1 Apply data pre-processing techniques such as standardization/normalization, transformation, aggregation, discretization/binarization, sampling etc. on any dataset Practical No. 2 Dataset: rain.csv

Download from data.gov.in: Rainfall in India

Lab 3: ( week of 16th, 23rd & 30thSeptember 2024 )

Q. NO. Program Practical No. Remarks
1 Writing/Review of Chapter 1, Chapter 3, and Chapter 4 of Project Report Project Work

Lab 4: ( week of 7th October 2024 )

Q. NO. Program Practical No. Remarks
1 Apply simple K-means algorithm for clustering any dataset. Compare the performance of clusters by varying the algorithm parameters. For a given set of parameters, plot a line graph depicting MSE obtained after each iteration. Practical No. 3 Dataset: Mall_Customers.csv

Download from data from kaggle: Mall Customer Segmentation Data

Projects

Team No. Project Title Team Members Outcomes/Remarks
1 Understanding the Monsoon Pattern in Eastern Gangatic Plain
  1. Akshary Sharma (25019)
  2. Abhay Yadav (25040)
  3. Anuj Gupta (25042)
  4. Amar Kumar (25065)
  5. Kunal Verma (25073)
  • Dataset:
  • Report:
  • Project Presentation:
2 NIRF Ranking Prediction
  1. Abhishek Prasad (25007)
  2. Vishal Kumar (25014)
  3. Nitish Kumar (25023)
  4. Anshu Kumar Dubey (25036)
  5. Sunny Chauhan (25050)
  • Dataset:
  • Report:
  • Project Presentation:
3 Student Performance Prediction
  1. Himanshu Kumar (25016)
  2. Kanan Pal (25072)
  3. Khushboo Yadav (25082)
  4. Diksha Joshi (25091)
  • Dataset:
  • Report:
  • Project Presentation:
4 FIFA Prediction
  1. Arihant (25003)
  2. Ayush Pundir (25027)
  3. Pratyush (25060)
  4. Ashish (25066)
  • Dataset:
  • Report:
  • Project Presentation:
5 Breast Cancer Prediction
  1. Vidhan (25044)
  2. Sandeep Kumar Sharma (25047)
  3. Ayushman Pandey (25094)
  4. Tanishk Panchal (25095)
  • Dataset:
  • Report:
  • Project Presentation:
6 YouTube spam comments classification
  1. Devesh Chauhan (25011)
  2. Shatrughan (25084)
  3. Om Ranjan (25085)
  4. Aman Sagar (25086)
  • Dataset:
  • Report:
  • Project Presentation:
7 Olympic Data Analysis and Prediction
  1. Kusum (25002)
  2. Aditya Kumar (25012)
  3. Divyanshi (25021)
  4. Tushar Rana (25064)
  • Dataset:
  • Report:
  • Project Presentation:
8 Credit Card Fraud Detection
  1. Ritesh Dhawan (25037)
  2. Bitthal Varshney (25041)
  3. Ansh Raj (25081)
  4. Uday Raj Verma (25083)
  5. Astitwa Rawat (25088)
  • Dataset:
  • Report:
  • Project Presentation:
9 CreditMap: Exploring Credit Score Patterns through Data Mining
  1. Himanshu Singh (25017)
  2. Garvit Kumar (25018)
  3. Mayank (25022)
  4. Abhishek Kumar Singh(25032)
  • Dataset:
  • Report:
  • Project Presentation:
10 Movie Recommendation System
  1. Tanya Agrahari (25030)
  2. Prakash Mishra (25035)
  3. Adarsh Singh (25074)
  4. Shivam Verma (25078)
  • Dataset:
  • Report:
  • Project Presentation:
11 Wine Quality Prediction
  1. Shivam Soni (250xx)
  2. ⁠Kashif (250xx)
  3. Akash Pathak (250xx)
  4. ⁠Priyanshu Sachan (250xx)
  • Dataset:
  • Report:
  • Project Presentation:
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