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Understanding the Problem Statement and Importing the Dataset
Performing basic EDA to get Insights into the data
Importing the necessary libraries
Using Info function to check for null values and datatypes
Imputing null values using suitable methods
Converting categorical values into numerical vectors
Plotting barplot of the dependent variable versus Independent variable
Using Boxplot for identifying outliers
Seperating dependent and Independent columns for training the model
Using train_test_split function for creating training and testing dataset
Understanding and Implementing Standardization
Applying ensemble model using Random Forest Classifier
Applying Decision Tree Classifier using AdaBoost
Applying ensembling model Voting Classifier
Applying Liner Model Logistic Regression
Plotting graphs for weight coefficients for different variables
Defining a function for performing Cross-Validation and calculating accuracy simultaneously
Applying Gradient Boosting Classifier and feature selection to extract best features for GBC
Extracting best features for Random Forest Classifier
Using the selected features for training the final model
Making predictions using the trained model and saving the predictions
Dream Housing Finance company deals in all home loans. They have a presence across all urban, semi-urban and rural areas. Customer first applies for the home loan after that company validates the customer eligibility for the loan.
The company wants to automate the loan eligibility process (real-time) based on customer detail provided while filling 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 to identify the customer's segments, those are eligible for loan amount so that they can specifically target these customers. Here they have provided a partial data set.