Analysis of the Restaurant reviews by using the Naive Bayes & the Random Forests Algorithms
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Updated
Aug 22, 2017 - Python
Analysis of the Restaurant reviews by using the Naive Bayes & the Random Forests Algorithms
An Artificial Neural Network with weight decay created using python using the Numpy library which can read handwritten digits. Uses K-Folds cross validation for training the Neural Network.
Predicts gender of an author based on syntactic constructions of their tweets
Code for Intro to ML Midterm 2
Progetto di Intelligenza artificiale sugli alberi di decisione con valori mancanti.
A Python script that implements Machine Learning Algorithm to predict if a female is affected by Breast Cancer after considering a certain set of features. The credit of the Dataset goes to UCI Repository of ML.
Does Work-Life Balance Matter? (DSI Capstone I Project)
Implementation of Naive Bayes, Gaussian Naive Bayes, and 5-fold cross-validation Nearest neighbor with pure python
Machine Learning Algorithms implemented using Numpy and Scipy
Codes and templates for ML algorithms created, modified and optimized in Python and R.
Machine Learning Task implemented in PySpark to parallelise K-Fold Cross Validation
This repository consists of 6 sections, detailing hands on Machine Learning Models: Regression, Classification, Clustering, AssocaitionRuleLearning, Deep Learning and Natural Language Processing Techniques
Linear Regression Feature Selection and Trainer
Insights into how the error varies with change in K while performing k-fold Cross Validation
ML model for stock trend prediction using Python
My very first hands on experiment with CV
GroupSplit is a module to help split datasets into train and test sets for data science and machine learning projects.
Collaborative filtering, word Embedding dense vector representation - NeuralNetwork regression model and K-Fold Evaluation.
Used Python Scikit-Learn to analyze Austin car crash data from 2018 to 2020 and created an interactive dashboard using a Random Forest Classifier algorithm to calculate a driver score from user features.
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