The aim of this project is: 1.Perform Text Classification using Multinomial Naive Bayes 2. Implement Naive Bayes from scratch for Text Classification. 3. Compare Results of self implemented code of Naive Bayes with one in Sklearn. dataset used is 20_newsgroups

Recommendation-Engine

A collection of Deep Learning And AI projects using Tensorflow and Keras

Identify the class of leaves by extracting the features from it. Using Convolutional Neural Network.

This group project aims to predict the arrest of different types of crime given a specific input (day/ location/etc.) using machine learning models.

A movie recommendation system based on the concept of collaborative filtering

The aim to decrease the maintenance cost of generators used in wind energy production machinery. This is achieved by building various classification models, accounting for class imbalance, and tuning on a user defined cost metric (function of true positives, false positives and false negatives predicted) & productionising the model using pipelines.

In this problem i have tried to explain how XGB algorithm works in case of classification. I have also stated the accuracy score at the end for our XGBClassifier model. The confusion matrix has also been shown for the same. I have used the Kaggle Dataset - Titanic Survivors csv file.

Predict the number of orders for the next Week in a restaurant.

Predict a person whether to develop diabetes using logistic regression

Predict if a woman will develop breast cancer_Ensemble Techniques_Stacking

[College Course] - Course: BITS F312 Neural Network and Fuzzy Logic

Reduce the time that cars spend on the test bench. Work with a dataset representing different permutations of features in a Mercedes-Benz car to predict the time it takes to pass testing. Optimal algorithms will contribute to faster testing, resulting in lower carbon dioxide emissions without reducing Mercedes-Benz’s standards.

Anticipation des besoins en consommation électrique de bâtiments (OpenClassrooms | Data Scientist | Projet 4)