The Titanic classification problem involves predicting whether a passenger on the Titanic survived or not, based on various features available about each passenger. The sinking of the Titanic in 1912 is one of the most infamous maritime disasters in history, and this dataset has been widely used as a benchmark for predictive modeling.

A simple AI recipe generator using ML and DL models 🍔🍨🍷

A Machine Learning basics course, containing pre-processing, scaling, and implementation of different models. Most implementation through sklearn.

Repository for different programs related to NLP

1- Importing the dataset 2- Handle missing data 3- Encode categorical data 4- Splitting dataset into Testing set and Train set 5- Feature Scaling

Library to Pre-process Images required by Deep Learning Algorithms. Normalizing, One Hot Encoding, Splitting Data, Reshaping, Conversions of data in different Dimensions as Required

This in-depth market basket analysis goes through a complete project cycle towards extracting valuable insights that the business can implement allowing them to scale. From preprocessing the data, to exploratory data analysis, association rule mining, interpretation and insights, and recommendations. This project was made to tackle these problems.

The project utilizes Natural Language Processing (NLP) techniques to preprocess and analyze video transcripts, and employs a BERT + Bi-GRU model for text classification. The code is implemented in a Google Colab notebook.

Repository for predicting house prices using the Ames Housing dataset. Implements advanced regression techniques with TensorFlow Decision Forests, including Random Forests. The project covers data exploration, feature engineering, model training, evaluation, and visualization.

I participated in this hackathon which provided data from thousands of restaurants in India regarding the time they take to deliver food for online order. Goal: predict the online order delivery time based on the given factors.

Load the dataset as dataframe using pandas . Handle missing values if needed . Encode categorical features if needed . Scale all the values between 0-1 with proper scaling technique. Split the dataset into features and labels. Use your intuition to determine which column indicates the labels.

A cnn based model to predict 3D object from 2D images

Basic commands in Python

We have analysis their drug addiction behavior. From this research work we can identify drug addiction behavior also. We have used classification model to classified different types of drug addiction people problem.

Machine Learning Data Preprocessing

This project is a part of Data Insight's Data Science Program 2021. This is an unguided project from DataCamp.

This is an AI tool that can classify the given image of resume as per the fields like Data Scientist,Civil Engineer etc.