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.
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.
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.
Use cluster analysis to identify the groups of characteristically similar schools in the College Scorecard dataset. Considerations: Clustering Algorithm Data Preparation How will you deal with missing values? Categorical variables? Feature intercorrelations? Feature normalization or scaling? Dimensionality reduction? Hyperparameters How will you set the parameters -- the algorithm's knobs and dials, so to speak -- in order to achieve valid and useful output? Interpretation Is it possible to explain what each cluster represents? Did you retain or prepare a set of features that enables a meaningful interpretation of the clusters? Do the compositions of the clusters seem to make sense? Validation How will you measure the validity of your clustering process? Which metrics will you use and how will you apply them?
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.
In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.
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.
In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.
In this Deep Learning Project on Image Segmentation Python, you will learn how to implement the Mask R-CNN model for early fire detection.
In this project you will use Python to implement various machine learning methods( RNN, LSTM, GRU) for fake news classification.