Loan-Eligibility-Prediction using multiple models

Binary Classification Problem

Steps:

1.Loading the dataset

Load the dataset into a pandas dataframe.

2.Data-Preprocessing

2.1.Handling missing values

Check if there are any missing values in the dataset. If there are any missing values, handle them using techniques like imputation.

2.2.Handling outliers

Check if there are any outliers in the dataset.

2.3.Feature Scaling using Standardscalar

Scale the features using StandardScaler.

2.4.Checking for imbalaned dataset

Check if the dataset is imbalanced. If it is imbalanced, handle it using techniques like oversampling or undersampling.

3.EDA

Perform exploratory data analysis to get insights about the dataset.

4.Splitting the dataset into train and test

Split the dataset into training and testing sets.

5.Building the model

5.1.Predicting

Using different models

5.2.Performance metrics

Evaluate the performance of the model using performance metric accuracy.