The goal of this data science project is to build a predictive model and find out the sales of each product at a given Big Mart store.
In this ML Project, you will use the Avocado dataset to build a machine learning model to predict the average price of avocado which is continuous in nature based on region and varieties of avocado.
Use the Amazon Reviews/Ratings dataset of 2 Million records to build a recommender system using memory-based collaborative filtering in Python.
This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.
Want to search images of clothes which have text on them? Then this project talks through how we can classify an image whether it has text on it or not. For this we use state of the model called as inception and try and deepdive into how it works on our dataset
CRNNs combine both convolutional and recurrent architectures and is widely used in text detection and optical character recognition (OCR). In this project, we are going to use a CRNN architecture to detect text in sample images. The data we are going to use is TRSynth100k from Kaggle. Given an image containing some text, the goal here is to correctly identify the text using the CRNN architecture. We are going to train the model end-to-end from scratch.
Machine Learning Project in R-Detect fraudulent click traffic for mobile app ads using R data science programming language.
In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.
This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.