Credit Risk Modelling in R
The main objective of this study is to implement the Credit Risk Modelling and build Credit models based on the significant variables in the dataset provided. Basically, we will implement the Credit Risk modelling in the R Studio and use the various concepts of Data Analytics such as Data Cleaning, Exploratory Data Visualization, Model Building and the evaluation of the models. We also use the techniques such as the Weights of Evidences and Information values for building a better and an accurate model. We will implement the Credit Risk Modelling using three algorithms which are- Logistic Regression, Random Forest and Neural Networks and will finally compare the three models based on their accuracy. After selecting the variables initially, we performed some Data Cleaning steps such as: ▸ Sub setting the required Variables from the full data set. ▸ Imputation of the Missing values. ▸ Converting the variables to their exact Data Types. ▸ Exploratory Data Visualization. Build the model.
Me concernant
Hello, I am Shivam. Welcome to my profile. Are you seeking a Data Science and Machine Learning professional who can deliver exceptional results? Look no further! With 5+ years of hands-on experience across multiple industries, I have honed my skills in Data Mining, Machine Learning, Deep Learning, Statistical Modelling, Computer Vision, and Natural Language Processing. ✅ Experienced Data Scientist & Machine Learning Expert ✅ Driving Results in Diverse Domains ✅ Committed to Your Success ✨ Why Choose Me? ✨ ✅ Proven Track Record: Successfully served 180+ satisfied clients, earning their trust and delivering outstanding outcomes. ✅ End-to-End Expertise: From ideation to implementation, I excel in managing the complete life cycle of Data Science and Machine Learning projects. ✅ Robust Skill Set: Proficient in Python and R, I possess strong capabilities in web scraping, database management, and developing integrated systems for advanced analytics and AI applications. ✅ Cutting-Edge Solutions: Skilled in Deep Learning, Computer Vision, and Big Data analytics, I can develop high-performance solutions tailored to your unique requirements. ✅ Full-Stack Proficiency: I build seamless pipelines for data manipulation, modeling, deployment, and optimization, ensuring smooth project execution. ✅ Data Visualization Mastery: Expertise in various data visualization tools to create compelling and impactful visual representations of insights. ✅ Algorithmic Prowess: Extensive knowledge of a wide range of machine learning algorithms, including regression, statistical modeling, classification, and clustering. ✅ Deep Neural Networks: Proficient in frameworks such as TensorFlow, Keras, and PyTorch, with hands-on experience in image processing, stock prediction, and predictive modeling. ✅ Business Intelligence Skills: Well-versed in tools like Tableau, QlikView, and QlikSense, enabling effective data-driven decision-making. ✅ Database Management Expertise: Experienced in handling MySQL and MongoDB for efficient data storage and retrieval. Passionate about driving business growth and leveraging data to generate valuable insights, I am committed to delivering exceptional results for your organization. Let's collaborate and unlock the true potential of your data! Contact me today to discuss your project requirements.
