Home Loan Approval

**Problem Statement**:

About Company
Dream Housing Finance company deals in all home loans. They have a presence across all urban, semi-urban and rural areas. The customer first applies for a home loan after that company validates the customer's eligibility for a loan.

**Problem**
The company wants to automate the loan eligibility process (real-time) based on customer detail provided while filling out the online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem identifying the customer segments eligible for loan amounts to target these customers specifically. Here they have provided a partial data set.

**Steps to Follow**:

1. Problem Statement
2. Hypothesis Generation
3. Getting the system ready and loading the data
4. Understanding the data
5. EDA
- Perform Univariate Analysis
- Perform Bivariate Analysis
6. Missing value and outlier treatment
7. Evaluation Metrics for classification problem
8. Model building: part 1 (Apply ML classification algorithms)
9. Feature engineering
10. Model building: part 2 (Apply ML classification algorithms)

{'Problem_Statement': '关于公司：Dream Housing Finance 公司致力于提供各类住房贷款服务，其业务遍及所有城市、准城市和农村地区。客户在填写在线申请表时，首先申请住房贷款，随后公司将验证客户的贷款资格。

**问题**：该公司希望建立一个基于客户在线申请表提供的详细信息的实时贷款资格自动化流程。这些详细信息包括性别、婚姻状况、教育程度、子女数量、收入、贷款金额、信用历史等。为了自动化这一流程，公司提出了一个识别符合贷款金额的顾客群体的问题，以便针对这些特定客户进行精准营销。在此，他们提供了一部分数据集。

**遵循步骤**：
1. 问题陈述
2. 假设生成
3. 准备系统和加载数据
4. 理解数据
5. 探索性数据分析（EDA）
- 执行单变量分析
- 执行双变量分析
6. 缺失值和异常值处理
7. 分类问题的评估指标
8. 模型构建：第一部分（应用机器学习分类算法）
9. 特征工程
10. 模型构建：第二部分（应用机器学习分类算法）'}