50_startups_prj3 multiple linear regression practical

To predict whether booked appointment will be completed or it will be no show.

Classified images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. The dataset was preprocessed, then trained a convolutional neural network on all the samples. I normalized the images, one-hot encoded the labels, built a convolutional layer, max pool layer, and fully connected layer.

Data Preprocessing for Machine Learning

This repository contains jupyter notebooks explaining the basics of TF and deep learning classification model using TF

Fast Encode Non-Numeric Variables into Dummy Columns

Automatic Response Generation to Conversational Stimuli

Implementation of decision tree from scratch along with analysis of its performance with different types of impurity measures

Employed hyper-parameter tuning (Gridsearch CV) and ensemble methods (Voting Classifier) to combine the results of the best models. Data Cleaning and Exploration using Pandas. Stratified Cross Validation to model and validate the training data

T20 World Cup Prediction System -- This GitHub repository contains the code for a T20 World Cup prediction system implemented in Python. The project utilizes popular libraries such as pandas, NumPy, and XGBoost for data manipulation, cleaning, and building predictive models.

Exploring machine learning with nueral networks for a charity analysis. Adjusting the model to try and improve accuracy to predict which projects are likely to be successful.

Python Machine Learning Projects | Hands-on Experience...

A host of data science + machine learning projects with Python, pandas, scikit-learn and more!

Feature Pre-processing

This project will focus on data preparation and will follow the steps : data cleaning, handling text and categorical attributes, and feature scaling.

This my entry for the Titanic competition on Kaggle. May 2019: public score is 0.80382, which is a top 10% ranking on the leader board of around 11.249 participants.