# Loan Data Analyzing in Python

• LOAN_DATASET.csv
• LOAN .ipynb
• In this project, finding unique values in every feature, finding maximum value minimum value of numerical_features, plotting histogram and plot histogram

Step:- 1

First, we upload the necessary libraries and then we upload the dataset

```import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns```
```LOAN = pd.read_csv("LOAN_DATASET.csv")
LOAN```
`LOAN.info()`
```print(f'Total no of empty values: {LOAN.isna().sum().sum()}')
LOAN.isna().sum()```
`LOAN.describe()`

Step:- 2

Finding unique values in every feature

``` def get_unq(LOAN):
for i in LOAN.columns:
print(f'{i} - {len(LOAN[i].unique())}')
get_unq(LOAN)```

Step:- 3

Finding maximum value minimum value of numerical_features

```def min_max(LOAN):
for i in LOAN.columns:
if LOAN[i].dtypes!='object':
print(f'{i} -> {sorted(list(LOAN[i]))[0]} to {sorted(list(LOAN[i]))[-1]}')
min_max(LOAN)```

Step:- 4

```# label encoder for categorical data
from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
LOAN['purpose'] = pd.DataFrame(encoder.fit_transform(LOAN['purpose']))```

Now we plot graph

```def histplo(df):
for i in LOAN.columns:
plt.figure(figsize=(5,7))
if i!= 'not.fully.paid':
sns.histplot(data=df,x = i,bins=30,kde = True,hue='not.fully.paid')
histplo(LOAN)
```
```plt.figure(figsize=(20,13))
sns.heatmap(LOAN.corr(),linewidths=0.5,annot= True)```