MIrandaStock

BlogWebsite

专业综合设计博客网站

DataAnalytics

Read the Universal Bank dataset. Ensure that necessary pre processing steps are implemented (wherever necessary). Type conversion Imputation Standardization Understand the spread of the data using the numerical attribute and see how the target is varying using the categorical attributes. Identify the important patterns using visualizations(not mandatory) Generate new features. Using PCA Load the data into h20 generate non linear features using Auto encoders Business understanding Consider only the required important attributes using Random Forest (including attributes which are Linear,non linear and business domain attributes). Built a regression model with income as target variable. Use the following technique. Linear Regression Decision Tree(Regression Tree) SVM Neural Network KNN Ada-boost Random Forest GBM Deep Learning. Built a classification model to predict those who are likely to accept the offer of a new personal loan, using personal loan as your target. Use the following technique. Logistic regression Decision tree (both C5.0,CART) SVM. Neural Network KNN Ada-boost Random Forest GBM Deep Learning Apply stacking on all the models for regression and classification.

dep-parser

first-website

一个只有前端的网站

learn-cpp

record learning cpp

Pyspark-CommunityDetection

利用pyspark实现LPA算法

roothub

the second upload

TensorRT-LLM

TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that execute those TensorRT engines.

MIrandaStock/Pyspark-CommunityDetection

利用pyspark实现LPA算法

MIrandaStock/BlogWebsite

专业综合设计博客网站

MIrandaStock/DataAnalytics

Read the Universal Bank dataset. Ensure that necessary pre processing steps are implemented (wherever necessary). Type conversion Imputation Standardization Understand the spread of the data using the numerical attribute and see how the target is varying using the categorical attributes. Identify the important patterns using visualizations(not mandatory) Generate new features. Using PCA Load the data into h20 generate non linear features using Auto encoders Business understanding Consider only the required important attributes using Random Forest (including attributes which are Linear,non linear and business domain attributes). Built a regression model with income as target variable. Use the following technique. Linear Regression Decision Tree(Regression Tree) SVM Neural Network KNN Ada-boost Random Forest GBM Deep Learning. Built a classification model to predict those who are likely to accept the offer of a new personal loan, using personal loan as your target. Use the following technique. Logistic regression Decision tree (both C5.0,CART) SVM. Neural Network KNN Ada-boost Random Forest GBM Deep Learning Apply stacking on all the models for regression and classification.

MIrandaStock/dep-parser

MIrandaStock/first-website

一个只有前端的网站

MIrandaStock/learn-cpp

record learning cpp

MIrandaStock/roothub

the second upload