arya.ai-assignment's Introduction

Arya.ai-Assignment

Exploratory Data Analysis

1. General Data Characteristics:

a. Size of Training Set - (3910, 58)
b. Size of Testing Set - (691, 57)
c. The column of responses i.e., ['Y'] is binary.
d. There are 57 predictors, none of which have informative headers.

2. Data Imbalance:

 There is no significant Data Imbalance in the dataset.

3. Missing Values:

a. There are no null values in any column (Missing data handling not required).
b. There is no column filled with all zeroes.

4. Outlier Detection:

  The dataset has a significant amount of noise which gives rise to a lot of outliers whose elimination is not necessary since “An Outlier is that observation which is significantly different from all other observations.” which is not the case.

5. Feature Co-relation using Heatmaps

6. Dimensionality Reduction using PCA:

 To handle highly co-related features since two or more collinear features (correlated in some way but not necessarily with a strictly linear relationship) can give unexpected results while computing feature importance.
 
 New Training Set Shape - (3910, 52)

6. Feature Importance using Random Forest Regressor

Final Training Set Shape - (3910, 36)

Methodology

1. Machine learning Classifiers

Step 1: Hyperparameter Tuning using GridSearch

Step 2: Training

Classifiers Trained

a. Logistic Regressor
b. K-nearest Neighbors
c. Support Vector Classifier
d. Random Forest Classifier
e. Extra Trees Classifier (BEST f1/auc score)
f. Stochastic Gradient Descent Classifier