This project is done to learn the fundamentals of data analytics that I learned in Data Analytics subject during Autumn semester of my MTech at IIT Kharagpur.
Analyze the data of bank loan defaulters and generate key insights that can help the bank to improve its loan default predictions.
The data is taken from Kaggle which is a dataset of bank loan defaulters.
Data dimensions: 3,07,511 records and 122 features
- Checked null value percentage in each column
- Checked the distribution of each column
- Checked the correlation between columns and target variable
- Removed unneccessary columns that do not contribute to predict target
- Visualized the count of defaulters and non-defaulters in the dataset for several categorical columns
- Standardized the data
- Binning the numerical columns to view the distribution of defaulters and non-defaulters according to ranges
- Replaced the null values with the measures of central tendency according to the type of column from NOIR classification
- Outlier detection using boxplots
- Drew insights by plotting the final features with target prepared after all the above steps (All the important insights are mentioned in the notebook)
- Clone the repository
- Install numpy, pandas, matplotlib, seaborn
- Run the notebook