For a given dataset, where class labels may not be found, choose the right target variable and discretize the same for classification. Find the dataset here: https://drive.google.com/file/d/1gu1ooPQzikhsIQNnTLPu_AMGcLUcP1o-/view

PART A: (5-marks)

Research Select the research paper of your choice. Attach the chosen paper along with the assignment submission.

Write a synopsis and find below pointers:

3. Paper Contribution

4. Data Pre-processing

5. Machine Learning Activity

6. Result analysis with metrics used from paper

7. Exploratory Data Analysis / Visualization

PART B: (15 marks) Dataset-based Implementation Refer to the dataset mapped against your group. Use python based APIs and perform the following three classes of activities.

EDA 1. Perform Exploratory Data Analysis to gather insight from the dataset. Write your inference about the analysis learned from visualizations (minimum 3) [3]

Classification CLASSIFICATION (any of the Logistic Regression / SVM / Decision Tree/ Nave Bayes/KNN/ANN). Justify your design choices at each step: Write as a markdown cell in jupyter notebook at the beginning of each subsection.

1. Perform and explain necessary pre-processing / feature engineering on this dataset [0.5]

2. Perform the Machine Learning activity. Explain the choice of target attribute, classification type, model selected with reason [1.5]

3. Quantify and explain the quality of your ML model. Explain the choice of evaluation metric [1.5]

4. Your observation about the results (Hint: comment on the problem statement and conclude the effectiveness of the machine learning activity) [0.5]

Regression Any of the Linear Regression (any of Gradient / Stochastic / MiniBatch)/linear basis models/KNN/Locally weighted regression/ any of the regularization techniques). Justify your design choices at each step: Write as a markdown cell in jupyter notebook at the beginning of each subsection.

1. Perform and explain necessary pre-processing / feature engineering on this dataset [0.5]

2. Perform the Machine Learning activity. Explain Attributes of interest, Regularization type with reason, model selected with reason [1.5]

3. Quantify and explain the quality of your ML model. Explain the choice of evaluation metric [1.5]

4. Your observation about the results (Hint: comment on the problem statement and conclude the effectiveness of the machine learning activity) [0.5]

Ensemble ML Justify your design choices at each step: Write as a markdown cell in jupyter notebook at the beginning of each subsection.

1. Perform and explain necessary pre-processing / feature engineering on this dataset [0.5]

2. Perform the Machine Learning activity. Explain Attributes of interest, base classifier chosen with reason, model selected with reason [1.5]

3. Quantify and explain the quality of your ML model. Explain the choice of evaluation metric [1.5]

4. Your observation about the results (Hint: comment on the problem statement and conclude the effectiveness of the machine learning activity) [0.5]