Loan Approval Prediction Using Random Forest Classifier | Machine Learning Project | Inttrvu.ai

In this video, Mr. Rohit Mande (Founder of Inttrvu.ai) implements the Random Forest Classifier algorithm step by step to predict whether a loan is approved or not. Basically, So what is Random Forest Algorithm? Random forests algorithm also known as random decision forests is an ensemble learning method. This algorithm is used for both classification and regression. For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the mean or average prediction of the individual trees is returned. In this video, you will learn the following: Information about libraries - numpy , pandas,sci-kit learn, seaborn and matplotlib which are needed for implementing machine learning model. Data gathering and Exploratory data Analysis(EDA) techniques. How to handle missing value in data set. Steps involved in Feature Creation. How to evaluate the Machine Learning Model. Concept of Feature Importance. How to predict result after training is completed. These are the some real time use cases of Random Forest Algorithm in Financial domain: Credit Scoring: Random forests are used to predict whether a person will likely pay back a loan or not, helping banks decide who to lend money to. Fraud Detection: They help identify suspicious activities in financial transactions, like unauthorized card usage, by analyzing patterns and anomalies. Customer Segmentation: Random forests divide bank customers into groups based on their behavior and preferences, allowing personalized services and targeted marketing campaigns. Stock Market Prediction: They analyze historical stock data to predict future price movements, assisting investors in making informed decisions. Video Timestamps: 00:00 Introduction 01:33 Importing Libraries 01:49 Data Gathering 03:25 EDA 06:20 Handling Missing Values 11:57 Feature Creation 13:50 Train-Test Split the Data 16:28 Fit & Evaluate the Model 21:57 Feature Importance 24:48 Predicting Probabilities for Each Class About Us: Rohit Mande is Founder and CEO of inttrvu.ai. He has 10+ years of professional experience as a Data Scientist. In his previous role as 'Chief Data Scientist at Barclays' he was leading a team of Data Scientists. He has done his Masters from Technical University of Darmstadt, Germany in 2013-2015. He is also having published patent applications listed on Google Patents. He is passionate about helping people in transitioning to Data Science role. Website : https://inttrvu.ai/ Instagram : https://www.instagram.com/inttrvu.ai/ LinkedIn : https://www.linkedin.com/in/rohit-mande-15a3a154/ Mail: info@inttrvu.ai Contact Number: +91 7756043707 Address :201, Navale Icon, Near Navale Bridge, Pune- 411041 #datascience #pythonprogramming #itindustry #datascientist #finance #machinelearning #forestclassifier #pythontutorial #projectfinance