homayoonkhadivi

-Churn-Bank-Customer-Model-Prediction-ANN

Churn Bank Customer Model Prediction/ANN

2-Creative-Deep-Learning-Models-Bike-Prediction

I am supper excited to share with you guys the results of new insights for the data preparation and building two models such as: Regression using SKLearn's Neural Network (NN) Train unisng Keras API with Tensorflow as Backend

Breast-Cancer-Datasets

Breast Cancer Datasets, Breast Cancer Prediction with SVM, PCA, Tuning

CNN-for-Mnist-Digit-Datasets-Top-18-

In this notebook I am using the MNIST Digits dataset. About the dataset: The dataset consists of 10 classes of handwritten Images pictures each with a number between 0-9.

Comprehensive-EDA-Models-hyperparameters-Heart

Classification Heart Disease¶ In this notebook, we will do a alot of data viasualization and classification of the heart disease

Cox-Proportional-Hazards-Survival-Analysis

Welcome to the Survival Analysis Notebook. In this notebook we'll develop risk models using survival data and a combination of linear and non-linear techniques. We'll be using a dataset with survival data of patients with Primary Biliary Cirrhosis (pbc). PBC is a progressive disease of the liver caused by a buildup of bile within the liver (cholestasis) that results in damage to the small bile ducts that drain bile from the liver. Our goal will be to understand the effects of different factors on the survival times of the patients. Along the way we'll learn about the following topics: Cox Proportional Hazards Data Preprocessing for Cox Models. Random Survival Forests Permutation Methods for Interpretation.

Exploratory-Data-Analysis-EDA-Innovative-3D-Visualization

Using Innovative Python Libraries to visualize and analyze the dataset in a comprehensive and 3D ways

HR-Analytics-EDA-Model-Building-Tuning

in this notebook, I want to analyze the Human Resource dataset. I will build different models and compare them with their gained accuracy.

Medical-Diagnosis-with-CNN-Transfer-Learning

With the help of CNN and Transfer Learning we will diagnosis the Pneumonia cancer from X_ray images

Natural-Language-Processing-NLP-for-Gender-Classification-from-Twitter-Dataset

With the help of Natural Language Processing NLP, I could identify the gender classification from the Twitter Dataset

homayoonkhadivi/Cox-Proportional-Hazards-Survival-Analysis

Welcome to the Survival Analysis Notebook. In this notebook we'll develop risk models using survival data and a combination of linear and non-linear techniques. We'll be using a dataset with survival data of patients with Primary Biliary Cirrhosis (pbc). PBC is a progressive disease of the liver caused by a buildup of bile within the liver (cholestasis) that results in damage to the small bile ducts that drain bile from the liver. Our goal will be to understand the effects of different factors on the survival times of the patients. Along the way we'll learn about the following topics: Cox Proportional Hazards Data Preprocessing for Cox Models. Random Survival Forests Permutation Methods for Interpretation.

homayoonkhadivi/Exploratory-Data-Analysis-EDA-Innovative-3D-Visualization

Using Innovative Python Libraries to visualize and analyze the dataset in a comprehensive and 3D ways

homayoonkhadivi/Medical-Diagnosis-with-CNN-Transfer-Learning

With the help of CNN and Transfer Learning we will diagnosis the Pneumonia cancer from X_ray images

homayoonkhadivi/-Churn-Bank-Customer-Model-Prediction-ANN

Churn Bank Customer Model Prediction/ANN

homayoonkhadivi/2-Creative-Deep-Learning-Models-Bike-Prediction

I am supper excited to share with you guys the results of new insights for the data preparation and building two models such as: Regression using SKLearn's Neural Network (NN) Train unisng Keras API with Tensorflow as Backend

homayoonkhadivi/Breast-Cancer-Datasets

Breast Cancer Datasets, Breast Cancer Prediction with SVM, PCA, Tuning

homayoonkhadivi/CNN-for-Mnist-Digit-Datasets-Top-18-

In this notebook I am using the MNIST Digits dataset. About the dataset: The dataset consists of 10 classes of handwritten Images pictures each with a number between 0-9.

homayoonkhadivi/Comprehensive-EDA-Models-hyperparameters-Heart

Classification Heart Disease¶ In this notebook, we will do a alot of data viasualization and classification of the heart disease

homayoonkhadivi/HR-Analytics-EDA-Model-Building-Tuning

in this notebook, I want to analyze the Human Resource dataset. I will build different models and compare them with their gained accuracy.

homayoonkhadivi/Natural-Language-Processing-NLP-for-Gender-Classification-from-Twitter-Dataset

With the help of Natural Language Processing NLP, I could identify the gender classification from the Twitter Dataset

homayoonkhadivi/Cancer_detection_application

Pneumonia Cancer Detection App with CNN and transfer learning models

homayoonkhadivi/Covid-19-Tweets-EDA-WordCloud-NLP-Sentiment-Analysis

homayoonkhadivi/Diabetes-Dataset-EDA-Building-Models-91-ROC

in this notebook, we want to predict if the patient has the disease (Diabetes) or not

homayoonkhadivi/farmer-master

Automating the ML project with CML capability & DVC automation

homayoonkhadivi/Learn-Amazon-SageMaker

Learn Amazon SageMaker

homayoonkhadivi/Lodge_191

Making a streamlit app for the people to add their information about visa lodge

homayoonkhadivi/powerbi_dashboard_sample

Data Professional Survey Dashboard

homayoonkhadivi/project

Add CML capability to have a update CI for your codebase

homayoonkhadivi/py

Repository to store sample python programs for python learning

homayoonkhadivi/services_provider

Making an application to show off all my skills and capabilities to other

homayoonkhadivi/Significant-EDA-Preprocessing-Result-House-Price

If you like it, Please upvote my notebook here https://www.kaggle.com/homayoonkhadivi/2-creative-deep-learning-models-bike-prediction

homayoonkhadivi/time_series_forecasting_application

Experienced with different smoothing approaches and ARIMA forecasting for a non-technical presentations

homayoonkhadivi/Titanic-EDA-Model-Hyperparameter-Tuning-Top-5-

The Challenge The sinking of the Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew. While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others. In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).